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# Copyright 2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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"""ImageNet preprocessing."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import logging

import tensorflow.compat.v1 as tf


IMAGE_SIZE = 224
CROP_PADDING = 32


def distorted_bounding_box_crop(image_bytes,
                                bbox,
                                min_object_covered=0.1,
                                aspect_ratio_range=(0.75, 1.33),
                                area_range=(0.05, 1.0),
                                max_attempts=100,
                                scope=None):
    """Generates cropped_image using one of the bboxes randomly distorted.

    See `tf.image.sample_distorted_bounding_box` for more documentation.

    Args:
      image_bytes: `Tensor` of binary image data.
      bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
          where each coordinate is [0, 1) and the coordinates are arranged
          as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
          image.
      min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
          area of the image must contain at least this fraction of any bounding
          box supplied.
      aspect_ratio_range: An optional list of `float`s. The cropped area of the
          image must have an aspect ratio = width / height within this range.
      area_range: An optional list of `float`s. The cropped area of the image
          must contain a fraction of the supplied image within in this range.
      max_attempts: An optional `int`. Number of attempts at generating a cropped
          region of the image of the specified constraints. After `max_attempts`
          failures, return the entire image.
      scope: Optional `str` for name scope.
    Returns:
      cropped image `Tensor`
    """
    with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]):
        shape = tf.image.extract_jpeg_shape(image_bytes)
        sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
            shape,
            bounding_boxes=bbox,
            min_object_covered=min_object_covered,
            aspect_ratio_range=aspect_ratio_range,
            area_range=area_range,
            max_attempts=max_attempts,
            use_image_if_no_bounding_boxes=True)
        bbox_begin, bbox_size, _ = sample_distorted_bounding_box

        # Crop the image to the specified bounding box.
        offset_y, offset_x, _ = tf.unstack(bbox_begin)
        target_height, target_width, _ = tf.unstack(bbox_size)
        crop_window = tf.stack(
            [offset_y, offset_x, target_height, target_width])
        image = tf.image.decode_and_crop_jpeg(
            image_bytes, crop_window, channels=3)

        return image


def _at_least_x_are_equal(a, b, x):
    """At least `x` of `a` and `b` `Tensors` are equal."""
    match = tf.equal(a, b)
    match = tf.cast(match, tf.int32)
    return tf.greater_equal(tf.reduce_sum(match), x)


def _resize_image(image, image_size, method=None):
    if method is not None:
        tf.logging.info('Use customized resize method {}'.format(method))
        return tf.image.resize([image], [image_size, image_size], method)[0]
    tf.logging.info('Use default resize_bicubic.')
    return tf.image.resize_bicubic([image], [image_size, image_size])[0]


def _decode_and_random_crop(image_bytes, image_size, resize_method=None):
    """Make a random crop of image_size."""
    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
    image = distorted_bounding_box_crop(
        image_bytes,
        bbox,
        min_object_covered=0.1,
        aspect_ratio_range=(3. / 4, 4. / 3.),
        area_range=(0.08, 1.0),
        max_attempts=10,
        scope=None)
    original_shape = tf.image.extract_jpeg_shape(image_bytes)
    bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3)

    image = tf.cond(
        bad,
        lambda: _decode_and_center_crop(image_bytes, image_size),
        lambda: _resize_image(image, image_size, resize_method))

    return image


def _decode_and_center_crop(image_bytes, image_size, resize_method=None):
    """Crops to center of image with padding then scales image_size."""
    shape = tf.image.extract_jpeg_shape(image_bytes)
    image_height = shape[0]
    image_width = shape[1]

    padded_center_crop_size = tf.cast(
        ((image_size / (image_size + CROP_PADDING)) *
         tf.cast(tf.minimum(image_height, image_width), tf.float32)),
        tf.int32)

    offset_height = ((image_height - padded_center_crop_size) + 1) // 2
    offset_width = ((image_width - padded_center_crop_size) + 1) // 2
    crop_window = tf.stack([offset_height, offset_width,
                            padded_center_crop_size, padded_center_crop_size])
    image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
    image = _resize_image(image, image_size, resize_method)
    return image


def _flip(image):
    """Random horizontal image flip."""
    image = tf.image.random_flip_left_right(image)
    return image


def preprocess_for_train(image_bytes,
                         use_bfloat16,
                         image_size=IMAGE_SIZE,
                         augment_name=None,
                         randaug_num_layers=None,
                         randaug_magnitude=None,
                         resize_method=None):
    """Preprocesses the given image for evaluation.

    Args:
      image_bytes: `Tensor` representing an image binary of arbitrary size.
      use_bfloat16: `bool` for whether to use bfloat16.
      image_size: image size.
      augment_name: `string` that is the name of the augmentation method
        to apply to the image. `autoaugment` if AutoAugment is to be used or
        `randaugment` if RandAugment is to be used. If the value is `None` no
        augmentation method will be applied applied. See autoaugment.py for more
        details.
      randaug_num_layers: 'int', if RandAug is used, what should the number of
        layers be. See autoaugment.py for detailed description.
      randaug_magnitude: 'int', if RandAug is used, what should the magnitude
        be. See autoaugment.py for detailed description.
      resize_method: resize method. If none, use bicubic.

    Returns:
      A preprocessed image `Tensor`.
    """
    image = _decode_and_random_crop(image_bytes, image_size, resize_method)
    image = _flip(image)
    image = tf.reshape(image, [image_size, image_size, 3])

    if augment_name:
        try:
            import autoaugment  # pylint: disable=g-import-not-at-top
        except ImportError as e:
            logging.exception('Autoaugment is not supported in TF 2.x.')
            raise e

        logging.info('Apply AutoAugment policy %s', augment_name)
        input_image_type = image.dtype
        image = tf.clip_by_value(image, 0.0, 255.0)
        image = tf.cast(image, dtype=tf.uint8)

        if augment_name == 'autoaugment':
            logging.info('Apply AutoAugment policy %s', augment_name)
            image = autoaugment.distort_image_with_autoaugment(image, 'v0')
        elif augment_name == 'randaugment':
            image = autoaugment.distort_image_with_randaugment(
                image, randaug_num_layers, randaug_magnitude)
        else:
            raise ValueError(
                'Invalid value for augment_name: %s' % (augment_name))

        image = tf.cast(image, dtype=input_image_type)

    image = tf.image.convert_image_dtype(
        image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)

    return image


def preprocess_for_eval(image_bytes,
                        use_bfloat16,
                        image_size=IMAGE_SIZE,
                        resize_method=None):
    """Preprocesses the given image for evaluation.

    Args:
      image_bytes: `Tensor` representing an image binary of arbitrary size.
      use_bfloat16: `bool` for whether to use bfloat16.
      image_size: image size.
      resize_method: if None, use bicubic.

    Returns:
      A preprocessed image `Tensor`.
    """
    image = _decode_and_center_crop(image_bytes, image_size, resize_method)
    image = tf.reshape(image, [image_size, image_size, 3])
    image = tf.image.convert_image_dtype(
        image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
    return image


def preprocess_image(image_bytes,
                     is_training=False,
                     use_bfloat16=False,
                     image_size=IMAGE_SIZE,
                     augment_name=None,
                     randaug_num_layers=None,
                     randaug_magnitude=None,
                     resize_method=None):
    """Preprocesses the given image.

    Args:
      image_bytes: `Tensor` representing an image binary of arbitrary size.
      is_training: `bool` for whether the preprocessing is for training.
      use_bfloat16: `bool` for whether to use bfloat16.
      image_size: image size.
      augment_name: `string` that is the name of the augmentation method
        to apply to the image. `autoaugment` if AutoAugment is to be used or
        `randaugment` if RandAugment is to be used. If the value is `None` no
        augmentation method will be applied applied. See autoaugment.py for more
        details.
      randaug_num_layers: 'int', if RandAug is used, what should the number of
        layers be. See autoaugment.py for detailed description.
      randaug_magnitude: 'int', if RandAug is used, what should the magnitude
        be. See autoaugment.py for detailed description.
      resize_method: 'string' or None. Use resize_bicubic in default.

    Returns:
      A preprocessed image `Tensor` with value range of [0, 255].
    """
    if is_training:
        return preprocess_for_train(
            image_bytes, use_bfloat16, image_size, augment_name,
            randaug_num_layers, randaug_magnitude, resize_method)
    else:
        return preprocess_for_eval(image_bytes, use_bfloat16, image_size,
                                   resize_method)
